from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-11-18 14:06:04.032345
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64(TODAY),
'red', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Wed, 18, Nov, 2020
Time: 14:06:07
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -42.2227
Nobs: 114.000 HQIC: -43.5062
Log likelihood: 1163.99 FPE: 5.32774e-20
AIC: -44.3828 Det(Omega_mle): 2.49972e-20
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.810423 0.221847 3.653 0.000
L1.Burgenland 0.148647 0.092400 1.609 0.108
L1.Kärnten -0.321227 0.077309 -4.155 0.000
L1.Niederösterreich -0.004599 0.223967 -0.021 0.984
L1.Oberösterreich 0.287046 0.181537 1.581 0.114
L1.Salzburg 0.112330 0.091748 1.224 0.221
L1.Steiermark 0.051325 0.130171 0.394 0.693
L1.Tirol 0.164668 0.085782 1.920 0.055
L1.Vorarlberg 0.019391 0.086211 0.225 0.822
L1.Wien -0.227020 0.177090 -1.282 0.200
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.840413 0.286636 2.932 0.003
L1.Burgenland -0.016887 0.119384 -0.141 0.888
L1.Kärnten 0.348062 0.099887 3.485 0.000
L1.Niederösterreich 0.059261 0.289375 0.205 0.838
L1.Oberösterreich -0.230562 0.234553 -0.983 0.326
L1.Salzburg 0.160065 0.118542 1.350 0.177
L1.Steiermark 0.187758 0.168187 1.116 0.264
L1.Tirol 0.134213 0.110835 1.211 0.226
L1.Vorarlberg 0.179321 0.111388 1.610 0.107
L1.Wien -0.623176 0.228808 -2.724 0.006
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.356945 0.094987 3.758 0.000
L1.Burgenland 0.103407 0.039562 2.614 0.009
L1.Kärnten -0.024034 0.033101 -0.726 0.468
L1.Niederösterreich 0.125720 0.095895 1.311 0.190
L1.Oberösterreich 0.258753 0.077728 3.329 0.001
L1.Salzburg 0.001529 0.039283 0.039 0.969
L1.Steiermark -0.060588 0.055735 -1.087 0.277
L1.Tirol 0.094370 0.036729 2.569 0.010
L1.Vorarlberg 0.146107 0.036912 3.958 0.000
L1.Wien 0.008370 0.075824 0.110 0.912
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.226504 0.114312 1.981 0.048
L1.Burgenland 0.001310 0.047611 0.028 0.978
L1.Kärnten 0.038147 0.039835 0.958 0.338
L1.Niederösterreich 0.085063 0.115405 0.737 0.461
L1.Oberösterreich 0.345852 0.093541 3.697 0.000
L1.Salzburg 0.094733 0.047275 2.004 0.045
L1.Steiermark 0.194805 0.067074 2.904 0.004
L1.Tirol 0.024880 0.044201 0.563 0.574
L1.Vorarlberg 0.111871 0.044422 2.518 0.012
L1.Wien -0.122154 0.091250 -1.339 0.181
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.970766 0.243677 3.984 0.000
L1.Burgenland 0.038701 0.101492 0.381 0.703
L1.Kärnten -0.017338 0.084916 -0.204 0.838
L1.Niederösterreich -0.141912 0.246006 -0.577 0.564
L1.Oberösterreich 0.044008 0.199400 0.221 0.825
L1.Salzburg 0.052636 0.100776 0.522 0.601
L1.Steiermark 0.097854 0.142980 0.684 0.494
L1.Tirol 0.236015 0.094223 2.505 0.012
L1.Vorarlberg 0.022559 0.094694 0.238 0.812
L1.Wien -0.263495 0.194516 -1.355 0.176
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.187969 0.170366 1.103 0.270
L1.Burgenland -0.045531 0.070958 -0.642 0.521
L1.Kärnten -0.012910 0.059369 -0.217 0.828
L1.Niederösterreich 0.216816 0.171994 1.261 0.207
L1.Oberösterreich 0.388597 0.139409 2.787 0.005
L1.Salzburg -0.033092 0.070457 -0.470 0.639
L1.Steiermark -0.050107 0.099964 -0.501 0.616
L1.Tirol 0.194766 0.065876 2.957 0.003
L1.Vorarlberg 0.047515 0.066205 0.718 0.473
L1.Wien 0.117159 0.135995 0.861 0.389
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.352716 0.217481 1.622 0.105
L1.Burgenland 0.057526 0.090581 0.635 0.525
L1.Kärnten -0.084703 0.075788 -1.118 0.264
L1.Niederösterreich -0.147257 0.219559 -0.671 0.502
L1.Oberösterreich -0.127191 0.177964 -0.715 0.475
L1.Salzburg -0.000786 0.089942 -0.009 0.993
L1.Steiermark 0.387521 0.127609 3.037 0.002
L1.Tirol 0.540033 0.084094 6.422 0.000
L1.Vorarlberg 0.215255 0.084514 2.547 0.011
L1.Wien -0.180045 0.173605 -1.037 0.300
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.288551 0.248290 1.162 0.245
L1.Burgenland 0.012307 0.103413 0.119 0.905
L1.Kärnten -0.074304 0.086524 -0.859 0.390
L1.Niederösterreich 0.201179 0.250663 0.803 0.422
L1.Oberösterreich 0.020412 0.203175 0.100 0.920
L1.Salzburg 0.222534 0.102684 2.167 0.030
L1.Steiermark 0.138682 0.145687 0.952 0.341
L1.Tirol 0.055892 0.096007 0.582 0.560
L1.Vorarlberg 0.000155 0.096486 0.002 0.999
L1.Wien 0.156523 0.198198 0.790 0.430
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.740497 0.135635 5.459 0.000
L1.Burgenland -0.008223 0.056492 -0.146 0.884
L1.Kärnten -0.011626 0.047266 -0.246 0.806
L1.Niederösterreich -0.078928 0.136931 -0.576 0.564
L1.Oberösterreich 0.255069 0.110990 2.298 0.022
L1.Salzburg 0.002658 0.056094 0.047 0.962
L1.Steiermark 0.005365 0.079585 0.067 0.946
L1.Tirol 0.076162 0.052446 1.452 0.146
L1.Vorarlberg 0.173852 0.052708 3.298 0.001
L1.Wien -0.137569 0.108271 -1.271 0.204
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.069535 -0.076863 0.190416 0.224440 0.025795 0.069381 -0.169297 0.066379
Kärnten 0.069535 1.000000 -0.084655 0.166561 0.030297 -0.167220 0.165821 -0.006359 0.249175
Niederösterreich -0.076863 -0.084655 1.000000 0.208881 0.021896 0.134463 0.069305 0.034002 0.347541
Oberösterreich 0.190416 0.166561 0.208881 1.000000 0.225353 0.261756 0.065981 0.047822 0.013448
Salzburg 0.224440 0.030297 0.021896 0.225353 1.000000 0.134711 0.026822 0.043320 -0.109911
Steiermark 0.025795 -0.167220 0.134463 0.261756 0.134711 1.000000 0.089501 0.100836 -0.221359
Tirol 0.069381 0.165821 0.069305 0.065981 0.026822 0.089501 1.000000 0.131897 0.078054
Vorarlberg -0.169297 -0.006359 0.034002 0.047822 0.043320 0.100836 0.131897 1.000000 0.052911
Wien 0.066379 0.249175 0.347541 0.013448 -0.109911 -0.221359 0.078054 0.052911 1.000000